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Altered Grey Wolf Optimization and Taguchi Method with FEA for Six-Phase Copper Squirrel Cage Rotor Induction Motor Design

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  • Chih-Hong Lin

    (Department of Electrical Engineering, National United University, Miaoli 360, Taiwan)

Abstract

This paper presents an altered grey wolf optimization, the Taguchi method, and finite element analysis (FEA) with two-phase multi-objective optimization for the design of a six-phase copper squirrel cage rotor induction motor (SCSCRIM). The multi-objective optimization design with high-performance property aims to achieve lower starting current, lower losses, lower input power, higher efficiency, higher output torque, and higher power factor. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the first-phase program is used for minimizing the starting current, stator iron loss, stator copper loss, and input power. The multi-objective optimization design with high-performance property using the altered grey wolf optimization, the Taguchi method, and FEA in the second-phase program is used for maximizing the efficiency, output torque, and power factor. Finally, the proposed skill with higher performances is evaluated and verified via a two-phase program design and some performance tests.

Suggested Citation

  • Chih-Hong Lin, 2020. "Altered Grey Wolf Optimization and Taguchi Method with FEA for Six-Phase Copper Squirrel Cage Rotor Induction Motor Design," Energies, MDPI, vol. 13(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2282-:d:354205
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    References listed on IDEAS

    as
    1. Barr, Nicholas, 2012. "Economics of the Welfare State," OUP Catalogue, Oxford University Press, edition 5, number 9780199297818.
    2. Chih-Hong Lin & Chang-Chou Hwang, 2018. "High Performances Design of a Six-Phase Synchronous Reluctance Motor Using Multi-Objective Optimization with Altered Bee Colony Optimization and Taguchi Method," Energies, MDPI, vol. 11(10), pages 1-14, October.
    3. Yuxin Sun & Jingwei Tang & Kai Shi, 2017. "Design of a Bearingless Outer Rotor Induction Motor," Energies, MDPI, vol. 10(5), pages 1-15, May.
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    Cited by:

    1. Lelisa Wogi & Amruth Thelkar & Tesfabirhan Tahiro & Tadele Ayana & Shabana Urooj & Samia Larguech, 2022. "Particle Swarm Optimization Based Optimal Design of Six-Phase Induction Motor for Electric Propulsion of Submarines," Energies, MDPI, vol. 15(9), pages 1-21, April.
    2. Krzysztof Tomczyk & Marek Sieja & Grzegorz Nowakowski, 2021. "Application of Identification Reference Nets for the Preliminary Modeling on the Example of Electrical Machines," Energies, MDPI, vol. 14(11), pages 1-15, May.
    3. Chih-Hong Lin, 2020. "Sage Revised Reiterative Even Zernike Polynomials Neural Network Control with Modified Fish School Search Applied in SSCCRIM Impelled System," Mathematics, MDPI, vol. 8(10), pages 1-30, October.
    4. Chih-Hong Lin, 2020. "A Rectified Reiterative Sieved-Pollaczek Polynomials Neural Network Backstepping Control with Improved Fish School Search for Motor Drive System," Mathematics, MDPI, vol. 8(10), pages 1-34, October.
    5. Marcel Torrent & Balduí Blanqué, 2021. "Influence of Equivalent Circuit Resistances on Operating Parameters on Three-Phase Induction Motors with Powers up to 50 kW," Energies, MDPI, vol. 14(21), pages 1-22, November.

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